Large Scale Data Integration

Large Scale Data Integration

Curtis Huttenhower

Sequence and Expression

01-24-08

Functional Relationships

• Two genes that work together to achieve similar cellular goals are functionally related

– Proteins that co-complex: ribosomal, polymerase, ORC, etc. etc.– A TF and its target– Two enzymes catalyzing different steps in the same metabolic pathway– A membrane-bound receptor and a downstream phos. target– etc. etc. etc.

• Genes that do really different stuff are considered to be functionally unrelated

• Anything else is neither– Genes with unknown function– Genes in similar but non-identical pathways

Functional Relationships

• How can we tell?These databases classify every gene pair into one of three groups:

• Functionally related• Unrelated• Neither

Data

• Well, as long as we’re talking about gene pairs, how do pairs of genes act in data?

ColocalizationYes/No

Two-hybridYes/No

AffinityYes/No

Shared miRNA sitesYes/No

Same chromosome band

Yes/No

Conserved TF sitesYes/No

MANY MANY microarraysCorrelation

High

Low

Data

• These fall into two general categories:– Yes/no (binary or 0/1)– Continuous (numerical scores)

Two-hybridYes/No

G1 G2 0G1 G3 1G1 G4 -G2 G3 -G2 G4 0G3 G4 1

MicroarraysCorrelation

G1 G2 0.9G1 G3 0.75G1 G4 0.1G2 G3 -0.1G2 G4 0.2G3 G4 -0.5

G1 G2 4G1 G3 3G1 G4 1G2 G3 0G2 G4 2G3 G4 0

Binning

Each dataset turns into a set of gene pairs labeled with small integers (or

nothing).

Integration

• Now, for each gene pair…

We have an “answer” indicating whether it’s a

related pair

G1 G2 1

And we have a bunch of datasets contributing their opinions(i.e. experimental results)

G1 G2 0

DS1

G1 G2 1

DS2

G1 G2 -

DS3

G1 G2 3

DSN

Integration

• Let’s look at each dataset individually:

Our answers know about a bunch of unrelated genes.

And a bunch of related genes.

G1 G3 0G3 G4 0G9 G14 0G10 G11 0…

G1 G2 1G1 G3 1G4 G7 1G10 G12 1…

Within each dataset, some subset of these

pairs have values:DS1

G1 G3 0G3 G4 -G9 G14 1G10 G11 0…

G1 G2 1G1 G3 0G4 G7 -G10 G12 1…

Integration

• Let’s look at each dataset individually:

Our answers know about a bunch of unrelated genes.

And a bunch of related genes.

G1 G3 0G3 G4 0G9 G14 0G10 G11 0…

G1 G2 1G1 G3 1G4 G7 1G10 G12 1…

Within each dataset, some subset of these

pairs have values:DS2

G1 G3 -G3 G4 0G9 G14 0G10 G11 -…

G1 G2 -G1 G3 1G4 G7 1G10 G12 1…

Integration

• Within each dataset, let’s count up the number of times each value occurs for each type of gene pair (related or unrelated):

G1 G3 0G3 G4 0G9 G14 0G10 G11 0…

G1 G2 1G1 G3 1G4 G7 1G10 G12 1…

DS1

G1 G3 0G3 G4 -G9 G14 1G10 G11 0…

G1 G2 1G1 G3 0G4 G7 -G10 G12 1…

96 9

13 36

Functionally related?

YesNo

Dataset value

0

1

Integration

• Within each dataset, let’s count up the number of times each value occurs for each type of gene pair (related or unrelated):

G1 G3 0G3 G4 0G9 G14 0G10 G11 0…

G1 G2 1G1 G3 1G4 G7 1G10 G12 1…

DS1

G1 G3 0G3 G4 -G9 G14 1G10 G11 0…

G1 G2 1G1 G3 0G4 G7 -G10 G12 1…

0.9 0.2

0.1 0.8

Functionally related?

YesNo

Dataset value

0

1

Integration

• Each dataset is now represented by two probability distributions:– One for related gene pairs, one for unrelated

DS1value

0 1

RelatedUnrelated

Pro

b.

DS5value

0 1

Prob.

2 3 4 5This is particularly

noticeable for continuous

datasets, where these represent

correlations.

Related genes are more likely to be highly correlated

Related genes are more likely to bind

Integration

• In the best case, datasets look like these:

• In the worst case, they look like these:0 1

Pro

b. RelatedUnrelated

0 1

Pro

b.

0 1

Pro

b.

0 1P

rob.

0 1

Pro

b.

Integration

• The variation in a dataset’s probability distribution indicates how informative it is.

• Some microarrays might look like these:

0 1

Prob.

2 3 4 5

Even if genes are highly correlated, it doesn’t mean

anything, because unrelated genes are also correlated.

0 1

Pro

b.

2 3 4 5

Everything’s really correlated! We can actually correct microarrays like this

during preprocessing.

Prediction

Ok, so what?• Given what we know about some genes,

we’ve learned something about datasets:

For each dataset Di, we know P(Di = d | FR)

• What we want to know is, given some data, what can we predict about unknown genes?

Prediction

• We know:

• And we want to know:

P(FR)The probability of a

gene pair being functionally related

The probability of each dataset

containing some valueP(D = d)

The probability of each value given a relationship (or not)

P(D = d | FR)

The probability that new genes are related

given some dataP(FR | D = d)

Prediction

• Enter Thomas Bayes:

• Who established Bayes’ theorem:

)(

)()|()|(

BP

APABPBAP

Prediction

<insert math here>

For each new gene pair, we can find its probability of being functionally related

Each dataset is weighted according to how informative we’ve

calculated it to beDatasets with no data for a particular gene

pair are ignored

And importantly for us, this all happens very quickly, regardless of the number

of genes or datasets

Prediction

• The result is that we produce a probability of functional relationship for each gene pair:

• Which in turns translates into a fully connected interaction network:

G1 G2 0.9G1 G3 0.75G1 G4 0.1G2 G3 0.3G2 G4 0.2…

Only the most confident edges are

typically shown

Context Specificity

• We can do even better!

• This process lets us figure out how much to “trust” each dataset.

• But datasets can give better (or worse) results in particular biological areas:

0 1

Pro

b.

2 3 4 5 0 1

Prob.

2 3 4 5

Microarrays for all gene pairs

Microarrays for ribosomal gene pairs

Context Specificity

• So we don’t just learn one probability distribution per dataset.

0 1

Pro

b.

Context Specificity


• We learn one probability distribution per dataset per biological process of interest!

0 1

Pro

b.

0 1 0 1 0 1

Carbonmetabolism Translation Autophagy

Context Specificity



• This means that for each gene pair, we can predict a different probability of relationship per process of interest.

0 1

Pro

b.

0 1 0 1 0 1


G1 G2 0.9G1 G3 0.75G1 G4 0.1G2 G3 0.3G2 G4 0.2…

G1 G2 0.15G1 G3 0.1G1 G4 0.9G2 G3 0.2G2 G4 0.25…

G1 G2 0.1G1 G3 0.2G1 G4 0.75G2 G3 0.15G2 G4 0.9…

Context Specificity



• This means that for each gene pair, we can predict a different probability of relationship per process of interest.

• Which in turn produces different interaction networks for each process.

0 1

Pro

b.

0 1 0 1 0 1


G1 G2 0.9G1 G3 0.75G1 G4 0.1G2 G3 0.3G2 G4 0.2…

G1 G2 0.15G1 G3 0.1G1 G4 0.9G2 G3 0.2G2 G4 0.25…

G1 G2 0.1G1 G3 0.2G1 G4 0.75G2 G3 0.15G2 G4 0.9…

Predicting Gene Function

Ok, so what?• We can now dig through these networks for

interesting things:

– YFG, its interaction partners, and their processes– Each edge represents specific datasets/publications– Dense clusters (new functional modules)– Areas that change a lot from process to process– What known disease genes are doing– Find relationships between TFs and their targets– Predicting function for uncharacterized genes


• Suppose we have a whole interaction network for autophagy:

• How do we predict new genes involved in the process?– Look at stuff “around” the known autophagy genes!


• The bioPIXIE algorithm:– Given a network and some query genes, find the other

genes most strongly connected to the whole query




G1: 0.5 + 0.5 + 0.1 = 1.1




G1: 0.5 + 0.5 + 0.1 = 1.1

G2: 0.9 + 0.9 + 0.5 = 2.3




G1: 0.5 + 0.5 + 0.1 = 1.1

G2: 0.9 + 0.9 + 0.5 = 2.3…




G1: 0.5 + 0.5 + 0.1 = 1.1

G2: 0.9 + 0.9 + 0.5 = 2.3…

Then display the genes with the best scores and the strongest edges connecting them


• The ratio algorithm:– Given a network and some query genes, find the other

genes most specifically connected to the whole query




G1: = 0.86/)5.01.09.0(

3/)(

0.10.50.5

0.10.50.5




G1: = 0.86/)5.01.09.0(

3/)(

0.10.50.5

0.10.50.5

G2: = 1.56/)1.01.05.0(

3/)(

0.50.90.9

0.50.90.9




…

Then display the genes with the best scores and the strongest edges connecting them

G1: = 0.86/)5.01.09.0(

3/)(

0.10.50.5

0.10.50.5

G2: = 1.56/)1.01.05.0(

3/)(

0.50.90.9

0.50.90.9


• These can differ a lot, particularly for “hubby” genes!


• These can differ a lot, particularly for “hubby” genes!

bioPIXIE

G1: 0.9 + 0.5 = 1.4

G2: 0.5 + 0.5 = 1.0

Ratio

G1: = 1.24/)1.09.0(

2/)(

0.50.9

0.50.9

G2: = 1.74/)1.01.0(

2/)(

0.50.5

0.50.5This difference is

exacerbated when the query isn’t itself strongly

connected, since it makes it easy for hubby

genes to dominate bioPIXIE’s results.


• How is this relevant to us?

• Suppose we ask about just a few genes– If they’re not internally consistent in the data,

bioPIXIE’s results are mostly hubs– This usually means that each predicted gene is

only related to one or two of the query genes


• This is a problem in the human genome, where our prior knowledge is relatively limited

• The ratio algorithm generates predictions that are targeted towards the commonalities of the query:

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Large Scale Data Integration